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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "最好使用V100的高RAM配置"
      ],
      "metadata": {
        "id": "iYXnu6ac-9dx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u6QIuNS4YvKf",
        "outputId": "b01575d0-c865-4ff1-ceb8-f1d3a2d6cd69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ChatGLM3'...\n",
            "remote: Enumerating objects: 1559, done.\u001b[K\n",
            "remote: Counting objects: 100% (669/669), done.\u001b[K\n",
            "remote: Compressing objects: 100% (133/133), done.\u001b[K\n",
            "remote: Total 1559 (delta 575), reused 537 (delta 536), pack-reused 890 (from 1)\u001b[K\n",
            "Receiving objects: 100% (1559/1559), 17.75 MiB | 16.55 MiB/s, done.\n",
            "Resolving deltas: 100% (900/900), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/THUDM/ChatGLM3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Inm7TvMZjJe",
        "outputId": "cabb2272-0de8-4c95-e29a-6929324ce9f1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ChatGLM3  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %cd ChatGLM3\n",
        "# !pip install -r requirements.txt\n",
        "!pip install transformers==4.39.3\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"THUDM/chatglm3-6b\", trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(\"THUDM/chatglm3-6b\", trust_remote_code=True, device='cuda')\n",
        "model = model.eval()\n"
      ],
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        "id": "Ut7IpeNcZhJH",
        "outputId": "f70b72cd-b826-41f5-ad0c-fb5759fc5371"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers==4.39.3\n",
            "  Downloading transformers-4.39.3-py3-none-any.whl.metadata (134 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/134.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (3.17.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (0.28.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (6.0.2)\n",
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            "Collecting tokenizers<0.19,>=0.14 (from transformers==4.39.3)\n",
            "  Downloading tokenizers-0.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.11/dist-packages (from transformers==4.39.3) (0.5.2)\n",
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            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.39.3) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.39.3) (4.12.2)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers==4.39.3) (2025.1.31)\n",
            "Downloading transformers-4.39.3-py3-none-any.whl (8.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m80.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading tokenizers-0.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m86.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.21.0\n",
            "    Uninstalling tokenizers-0.21.0:\n",
            "      Successfully uninstalled tokenizers-0.21.0\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.48.2\n",
            "    Uninstalling transformers-4.48.2:\n",
            "      Successfully uninstalled transformers-4.48.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "sentence-transformers 3.4.1 requires transformers<5.0.0,>=4.41.0, but you have transformers 4.39.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed tokenizers-0.15.2 transformers-4.39.3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "4b01420d50e8448a949d45fc6128bbf7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- tokenization_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e98598e0c3d548fcaddc637b643cb58a"
            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4b03988f920b42fbbcb06a98e2624889"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting eos_token is not supported, use the default one.\n",
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting pad_token is not supported, use the default one.\n",
            "WARNING:transformers_modules.THUDM.chatglm3-6b.e9e0406d062cdb887444fe5bd546833920abd4ac.tokenization_chatglm:Setting unk_token is not supported, use the default one.\n"
          ]
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8f6f732e20434a229350ec70714bb09b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e34d0628ff2f4690b85b204eff1d06a9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- configuration_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45877b23952f49f9a9f4b97ae09cb3cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- quantization.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm3-6b:\n",
            "- modeling_chatglm.py\n",
            "- quantization.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/file_download.py:795: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "60ca1f4ea5b94c50b9ee04a30c981535"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
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            }
          },
          "metadata": {}
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          "metadata": {}
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            }
          },
          "metadata": {}
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          "metadata": {}
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          },
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a9e5fabf71f4768839fbb62764af632"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2c8e7wKbddw2",
        "outputId": "3f90deff-23d3-446d-bfa9-7c5d049c879f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChatGLMForConditionalGeneration(\n",
              "  (transformer): ChatGLMModel(\n",
              "    (embedding): Embedding(\n",
              "      (word_embeddings): Embedding(65024, 4096)\n",
              "    )\n",
              "    (rotary_pos_emb): RotaryEmbedding()\n",
              "    (encoder): GLMTransformer(\n",
              "      (layers): ModuleList(\n",
              "        (0-27): 28 x GLMBlock(\n",
              "          (input_layernorm): RMSNorm()\n",
              "          (self_attention): SelfAttention(\n",
              "            (query_key_value): Linear(in_features=4096, out_features=4608, bias=True)\n",
              "            (core_attention): CoreAttention(\n",
              "              (attention_dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (dense): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          )\n",
              "          (post_attention_layernorm): RMSNorm()\n",
              "          (mlp): MLP(\n",
              "            (dense_h_to_4h): Linear(in_features=4096, out_features=27392, bias=False)\n",
              "            (dense_4h_to_h): Linear(in_features=13696, out_features=4096, bias=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (final_layernorm): RMSNorm()\n",
              "    )\n",
              "    (output_layer): Linear(in_features=4096, out_features=65024, bias=False)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "for idx, (key, value) in enumerate(model.named_parameters()):\n",
        "    print(f\"{key:^40} paramerters num:  {value.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UwaEgwzfWW2",
        "outputId": "3af19cd9-3034-48f4-d59d-2d69a2b029e4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "transformer.embedding.word_embeddings.weight paramerters num:  torch.Size([65024, 4096])\n",
            "transformer.encoder.layers.0.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.0.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.0.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.0.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.0.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.0.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.0.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.1.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.1.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.1.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.1.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.1.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.1.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.1.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.2.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.2.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.2.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.2.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.2.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.2.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.2.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.3.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.3.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.3.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.3.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.3.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.3.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.3.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.4.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.4.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.4.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.4.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.4.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.4.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.4.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.5.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.5.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.5.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.5.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.5.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.5.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.5.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.6.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.6.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.6.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.6.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.6.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.6.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.6.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.7.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.7.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.7.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.7.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.7.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.7.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.7.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.8.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.8.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.8.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.8.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.8.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.8.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.8.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.9.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.9.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.9.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.9.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.9.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.9.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.9.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.10.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.10.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.10.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.10.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.10.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.10.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.10.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.11.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.11.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.11.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.11.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.11.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.11.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.11.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.12.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.12.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.12.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.12.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.12.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.12.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.12.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.13.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.13.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.13.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.13.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.13.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.13.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.13.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.14.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.14.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.14.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.14.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.14.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.14.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.14.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.15.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.15.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.15.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.15.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.15.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.15.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.15.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.16.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.16.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.16.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.16.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.16.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.16.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.16.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.17.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.17.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.17.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.17.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.17.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.17.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.17.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.18.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.18.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.18.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.18.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.18.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.18.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.18.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.19.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.19.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.19.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.19.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.19.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.19.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.19.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.20.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.20.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.20.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.20.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.20.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.20.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.20.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.21.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.21.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.21.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.21.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.21.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.21.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.21.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.22.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.22.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.22.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.22.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.22.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.22.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.22.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.23.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.23.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.23.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.23.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.23.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.23.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.23.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.24.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.24.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.24.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.24.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.24.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.24.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.24.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.25.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.25.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.25.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.25.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.25.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.25.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.25.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.26.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.26.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.26.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.26.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.26.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.26.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.26.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.layers.27.input_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.27.self_attention.query_key_value.weight paramerters num:  torch.Size([4608, 4096])\n",
            "transformer.encoder.layers.27.self_attention.query_key_value.bias paramerters num:  torch.Size([4608])\n",
            "transformer.encoder.layers.27.self_attention.dense.weight paramerters num:  torch.Size([4096, 4096])\n",
            "transformer.encoder.layers.27.post_attention_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "transformer.encoder.layers.27.mlp.dense_h_to_4h.weight paramerters num:  torch.Size([27392, 4096])\n",
            "transformer.encoder.layers.27.mlp.dense_4h_to_h.weight paramerters num:  torch.Size([4096, 13696])\n",
            "transformer.encoder.final_layernorm.weight paramerters num:  torch.Size([4096])\n",
            "    transformer.output_layer.weight      paramerters num:  torch.Size([65024, 4096])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data=input(\"请输入:\")\n",
        "response,history = model.chat(tokenizer, 'AI如何学习', history=[])\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBhQClVRZGXG",
        "outputId": "6de97b17-2f87-4dc6-8c67-1dea7a110223"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AI的学习过程主要分为以下几个步骤：\n",
            "\n",
            "1. 数据收集：AI系统需要大量的数据来训练。数据可以来源于各种渠道，如网络、数据库、传感器等。数据质量对AI模型的效果至关重要，因此数据清洗和预处理是必不可少的步骤。\n",
            "\n",
            "2. 数据标注：在训练之前，AI系统需要对数据进行标注，以便让模型了解数据的结构和含义。标注过程通常需要专业知识和经验，如图像标注、文本标注等。\n",
            "\n",
            "3. 模型设计：根据任务需求，AI系统需要设计合适的模型。这包括选择合适的算法、网络结构和参数配置等。模型设计是AI学习的关键环节，直接关系到模型的效果和性能。\n",
            "\n",
            "4. 模型训练：利用收集到的数据和设计好的模型进行训练。训练过程中，AI系统会通过反向传播算法更新模型参数，使得模型能够逐渐逼近预期的目标。训练过程可能需要进行多轮迭代，以达到最佳的模型效果。\n",
            "\n",
            "5. 模型评估：在训练过程中或训练完成后，需要对模型进行评估，以检查其性能是否达到预期。评估方法包括准确率、召回率、F1分数等指标。根据评估结果，可以进一步调整模型参数或改进算法。\n",
            "\n",
            "6. 模型优化：根据评估结果，对模型进行优化。这可能包括调整网络结构、增加训练数据、改进训练方法等。优化过程可能需要多次尝试和迭代。\n",
            "\n",
            "7. 模型应用：经过训练和优化后的模型可以应用于实际场景，如智能客服、智能推荐、图像识别等。在实际应用中，AI系统需要不断地接收用户输入或环境数据，并根据模型生成的结果进行相应的响应。\n",
            "\n",
            "AI学习是一个持续迭代的过程，通过不断地训练、评估、优化和应用，AI系统会逐渐提高其性能，为人类带来更多的便利和价值。\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#挂载谷歌云盘\n",
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "6AF3_UrijO8x"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#查看huggingface下载的模型，并复制到谷歌云盘里\n",
        "import os\n",
        "# os.listdir('/root/.cache/huggingface/hub/models--THUDM--chatglm3-6b')\n",
        "# #os.cp 上面路径内容到 /content/drive/下面\n",
        "# os.system('cp -r /root/.cache/huggingface/hub/models--THUDM--chatglm3-6b /content/drive/MyDrive/')"
      ],
      "metadata": {
        "id": "7s6Zv6k7kB08"
      },
      "execution_count": 7,
      "outputs": []
    }
  ]
}